Applied Data Science
Participants will develop a deep and profound understanding of Big Data Analytics and the Data Science lifecycle, and the ability to develop a wide range of machine learning models (in the areas of Regression, Classification and Clustering), optimize and evaluate model quality. Participants will also gain the experience of using Natural Language Processing (NLP) techniques, and building Product Recommendation Systems.
This course caters to those with some experience in Data Analytics and Data Science, have the intentions of becoming full-fledged Data Scientists. Participants should preferably have some knowledge in Python Programming and are recommended to have completed the Applied Data Analytics course.
- Data Science Overview
- Data Science Lifecycle
- Data Acquisition & Cleansing
- Data Analysis & Statistical Methods
- Introduction to Machine Learning
- Introduction to ML Libraries
- Machine Learning Models
- Predictive Models (Multiple Linear Regression)
- Classification Models (Logistic Regression, KNN, SVM, Random Forest)
- Clustering Models (K-Means, DBSCAN)
- Natural Language Processing (NLP) and Sentiment Analysis
- Recommender Systems (Apriori, SVD)
- Model Evaluation and Optimization (Confusion Matrix, Accuracy, Precision, Recall, F-Score, Cross Validation, Stratified Sampling and Learning Curves)